

'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

_UpperCAmelCase  :		str  =		logging.get_logger(__name__)

_UpperCAmelCase  :		Optional[int]  =		{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'imagegpt'
 UpperCamelCase__     =       ['past_key_values']
 UpperCamelCase__     =       {
     'hidden_size': 'n_embd',
     'max_position_embeddings': 'n_positions',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }







 def __init__(       self  , snake_case_=5_12 + 1  , snake_case_=32 * 32  , snake_case_=5_12  , snake_case_=24  , snake_case_=8  , snake_case_=None  , snake_case_="quick_gelu"  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=1E-5  , snake_case_=0.02  , snake_case_=True  , snake_case_=True  , snake_case_=False  , snake_case_=False  , snake_case_=False  , **snake_case_  , ):
   lowercase									=vocab_size
   lowercase									=n_positions
   lowercase									=n_embd
   lowercase									=n_layer
   lowercase									=n_head
   lowercase									=n_inner
   lowercase									=activation_function
   lowercase									=resid_pdrop
   lowercase									=embd_pdrop
   lowercase									=attn_pdrop
   lowercase									=layer_norm_epsilon
   lowercase									=initializer_range
   lowercase									=scale_attn_weights
   lowercase									=use_cache
   lowercase									=scale_attn_by_inverse_layer_idx
   lowercase									=reorder_and_upcast_attn
   lowercase									=tie_word_embeddings

   super().__init__(tie_word_embeddings=snake_case_  , **snake_case_   )

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 @property
 def 						_A(       self   ):
   return OrderedDict(
       [
           ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
       ]   )







 def 						_A(       self  , snake_case_  , snake_case_ = 1  , snake_case_ = -1  , snake_case_ = False  , snake_case_ = None  , snake_case_ = 3  , snake_case_ = 32  , snake_case_ = 32  , ):
   lowercase									=self._generate_dummy_images(snake_case_  , snake_case_  , snake_case_  , snake_case_   )
   lowercase									=dict(preprocessor(images=snake_case_  , return_tensors=snake_case_   )   )

   return inputs






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FNetForMaskedLM",
      "FNetForMultipleChoice",
      "FNetForNextSentencePrediction",
      "FNetForPreTraining",
      "FNetForQuestionAnswering",
      "FNetForSequenceClassification",
      "FNetForTokenClassification",
      "FNetLayer",
      "FNetModel",
      "FNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



